Outbreak Response and Incident Management: SHEA Guidance and Resources for Healthcare Epidemiologists in United States Acute-Care Hospitals
Bibliographic record
Abstract
This expert guidance document was developed as a resource to provide healthcare epidemiologists working in acute-care hospitals with a high-level overview of incident management for infectious diseases outbreaks and to prepare them to work within an emergency response framework.It addresses how the epidemiologist's skills and expertise apply to scenarios that require enhanced preparedness and response efforts, eg, when pathogens associated with outbreaks are poorly characterized or when outbreaks require additional interventions including, but not limited to, healthcare personnel education, enhanced infection prevention and control measures, added staffing, supplies, and resources, adjustments to clinical and support activities, and external communications.Its recommendations are not pathogen-specific and are meant to apply to a range of potential infectious diseases outbreaks.To provide high-level guidance and context for incident management, the authors specify recommendations for the healthcare epidemiologist, as well as involvement and responsibilities of the facility and other healthcare personnel (HCP). authorsIn May 2016, SHEA members submitted online applications for the Society for Healthcare Epidemiology of America (SHEA)/Centers for Disease Control and Prevention (CDC) Outbreak Response Training Program (ORTP) panels, which were responsible for the content development for the multifaceted educational, training, and guidance opportunities provided by the ORTP: Advisory Panel, Expert Guidance Panel, and Education Panel.Panel members were selected based on their expertise in outbreak response, professional background, and involvement with SHEA and other organizations working in outbreak response and relevant fields.Each panel was composed of multidisciplinary experts in public health, emergency medicine, hospital medicine, community medicine, microbiology, pediatrics, and long-term care, with additional expertise in infectious diseases including Ebola virus disease (EVD), pandemic influenza, carbapenem-resistant Enterobacteriaceae (CRE), multidrug-resistant tuberculosis (MDR-TB), Middle East respiratory syndrome-coronavirus (MERS-CoV), severe acute respiratory syndrome (SARS), and Zika virus.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".